Proposes a source-data-free transfer learning framework for sparse single-index models that transfers generalized Stein's lemma summaries and uses a guided MLP for nonlinear adaptation.
arXiv preprint arXiv:2003.12724 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
CmIR uses causal inference to separate invariant causal representations from spurious ones in multimodal data, improving generalization under distribution shifts and noise via invariance, mutual information, and reconstruction constraints.
citing papers explorer
-
Multi-Source Transfer Learning of Sparse Single-Index Models
Proposes a source-data-free transfer learning framework for sparse single-index models that transfers generalized Stein's lemma summaries and uses a guided MLP for nonlinear adaptation.
-
Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective
CmIR uses causal inference to separate invariant causal representations from spurious ones in multimodal data, improving generalization under distribution shifts and noise via invariance, mutual information, and reconstruction constraints.